Learnings from a Machine Learning Engineer — Part 4: The Model | by David Martin | Jan, 2025

Sensible insights for a data-driven strategy to mannequin optimization

On this final a part of my sequence, I’ll share what I’ve discovered on choosing a mannequin for picture classification and easy methods to superb tune that mannequin. I will even present how one can leverage the mannequin to speed up your labelling course of, and eventually easy methods to justify your efforts by producing utilization and efficiency statistics.

In Half 1, I mentioned the method of labelling your picture knowledge that you just use in your picture classification challenge. I confirmed how outline “good” pictures and create sub-classes. In Half 2, I went over varied knowledge units, past the same old train-validation-test units, with benchmark units, plus easy methods to deal with artificial knowledge and duplicate pictures. In Half 3, I defined easy methods to apply completely different analysis standards to a educated mannequin versus a deployed mannequin, and utilizing benchmarks to find out when to deploy a mannequin.

Mannequin choice

Thus far I’ve centered a variety of time on labelling and curating the set of pictures, and in addition evaluating mannequin efficiency, which is like placing the cart earlier than the horse. I’m not attempting to attenuate what it takes to design an enormous neural community — it is a crucial a part of the applying you’re constructing. In my…